As digital camera related technologies advance rapidly in recent years, various electronic devices (such as digital cameras, digital camcorders, notebook computers, mobile phones and webcams, etc) are introduced constantly to the market. Not only the quality becomes increasingly higher, but the volume of products also becomes increasingly smaller, and thus their market price becomes lower gradually. Therefore, these electronic image capturing devices available in the market become popular. Although many digital imaging devices are equipped with advanced functions such as an auto focus and an auto exposure, an image information can be obtained after a whole scene is sensed to determine whether or not to capture the image information, and a face only occupies a small portion of the whole scene, and thus a novice having little experience and skill of properly adjusting a shutter and a diaphragm cannot capture satisfactory and praised images. Therefore, it is an important subject for electronic imaging device designers and manufacturers to find a way of designing an electronic imaging device with a smart imaging function to meet the photographic requirements of consumers, compensating the consumers' insufficient skills of taking a picture, effectively saving the long adjusting procedure and time, and taking high-quality images.
To achieve an electronic imaging device with a smart imaging function and capable of taking high quality images, some manufactures have applied face detection technologies to the new types of electronic imaging devices, wherein the algorithm for detecting a face has been disclosed in many publications, and the most popular one is the face detector designed according to a Gentle Adaboost (GAB) algorithm, and the face detector uses a Haar-like feature to identify a face and a specific quantity of face pattern samples to train a required face classifier to determine which image of the scene belongs (or not belongs) to a face, so as to detect a face in the image and provide a quick identification. However, if a new model of electronic capturing device having a face detector is used for detecting and recognizing a face in a preview image, the face detector must be able to perform a detecting process for the whole image and complete a huge numerical computation in order to detect an unknown face newly present or already resided in a previous frame (or a current frame), and thus the processing speed will be slowed down significantly. An image of 120×160 pixels is taken for example, and a traditional face detector uses ten searching windows of different dimensions for searching a face in a preview image, and the dimensions of the searching windows are modified horizontally and vertically one by one and moved horizontally and vertically on the whole image, so as to search a face repeatedly. Therefore, the number of computations involved in the detecting process is huge and causes poor detection speed and efficiency, and such arrangement cannot meet customer requirements and definitely requires improvements.